{"id":"W3153058972","doi":"10.31442/0235-2494-2021-0-3-56-62","title":"Climate features of 2020 in Russia and their impact on agricultural prices and production","year":2021,"lang":"en","type":"article","venue":"Economy of agricultural and processing enterprises","topic":"Agricultural Development and Policies","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Agriculture; Quarter (Canadian coin); Agricultural economics; Agricultural productivity; Production (economics); Coronavirus disease 2019 (COVID-19); Pandemic; Economics; Business; Natural resource economics; Geography; Macroeconomics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008842582,0.0002466779,0.0003669565,0.00001743454,0.0001418234,0.00009587436,0.00007831118,0.00008027053,0.00001472846],"category_scores_gemma":[0.00002152938,0.00006840771,0.00006223573,0.000249775,0.0001063344,0.0003879151,0.00009344515,0.000108335,3.74766e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001396403,"about_ca_system_score_gemma":0.000007621277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004872495,"about_ca_topic_score_gemma":0.0001756395,"domain_scores_codex":[0.9990469,0.00003665237,0.0003013859,0.0003237941,0.00007041947,0.0002208879],"domain_scores_gemma":[0.9994985,0.00009128002,0.0002177436,0.00002638021,0.00009145544,0.0000745764],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002276306,0.000314283,0.3506741,0.0006852714,0.0001066938,0.000003603833,0.006948954,0.00002385938,0.3481162,0.000324646,0.001382741,0.291192],"study_design_scores_gemma":[0.0001206704,0.0001471699,0.9681433,0.0002808685,0.00001064956,0.00007450023,0.002806679,0.000001267597,0.02780831,0.0001782845,0.0002427448,0.0001855922],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925344,0.003571098,4.450512e-8,0.002091059,0.00004136513,0.0001443759,0.00002774863,0.0000203182,0.00156958],"genre_scores_gemma":[0.9982227,0.001311783,0.00005886889,0.00005451406,0.0001136203,0.000008890343,0.00006756905,8.934217e-7,0.0001611809],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6174691,"threshold_uncertainty_score":0.2789586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007240329417250211,"score_gpt":0.2074284046513805,"score_spread":0.2001880752341303,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}